Government Revenue Forecasting in Nepal

Transcription

1 Governmen Revenue Forecasing in Nepal T. P. Koirala, Ph.D.* Absrac This paper aemps o idenify appropriae mehods for governmen revenues forecasing based on ime series forecasing. I have uilized level daa of monhly revenue series including 92 observaions saring from 997 o 202 for he analysis. Among he five compeiive mehods under scruiny, Winer mehod and Seasonal ARIMA mehod are found in racking he acual Daa Generaing Process (DGP) of monhly revenue series of he governmen of Nepal. Ou of wo seleced mehods, seasonal ARIMA mehod albei superior in erms of minimum MPE and MAPE crieria. However, he resuls of forecased revenues in his paper may vary depending on he applicaion of more sophisicaed mehods of forecasing which capure cyclical componens of he revenue series. The prevailing forecasing mehod based paricularly on growh rae mehod exended wih discreionary adjusmen of a number of updaed assumpions and personal judgmen can creae uncerainy in revenue forecasing pracice. Therefore, he mehods recommended here in his paper help in reducing forecasing error of he governmen revenue in Nepal. Key words: Daa generaing process, forecas bias, seasonal paern, under-or-over esimaion, governmen revenue, seasonaliy JEL Classificaion: H2, O23 * Assisan Direcor, Research Deparmen, Nepal Rasra Bank. Acknowledgemen: I would like o hank Ediorial Board of NRB Economic Review and anonymous referees for heir consrucive commens on he paper.

2 48 NRB ECONOMIC REVIEW I. INTRODUCTION The revenue forecass by he naional governmen are carried ou in course of budge preparaion. An accuracy of revenue forecass is one key issue in he design and execuion of fiscal policies (IMF, 200). Under or over-predicion of revenue forecas creaes budge planning vulnerable. Revenue forecas provides necessary discipline for negoiaions beween he execuive and legislaive branches of he governmen. I helps in seing up performance arges for revenue deparmens and agencies (Auerbach, 999, Danninger, 2005). One of he major sources of error (or bias) in revenue forecasing is he mehods adoped in forecasing revenue in addiion o variey of poliical and insiuional facors deermining such bias (Golosov and Kind, 2002, Kyobe and Danninger, 2005). In Nepal, revenue forecass is an imporan ask of Minisry of Finance (MOF) in he course of budge preparaion and specifying performance arges of revenue collecion offices. Major insiuions involved in forecasing revenue in he counry are MOF and Nepal Rasra Bank (NRB) as heir work of forecasing is an essenial par of he budgeary process. The IMF, especially is Fiscal Affairs Deparmen (FAD) ofen gives advice for a sysemaic analysis of forecasing in low-income counries in he conex of reforms on he budge planning process (Kyobe and Danninger, 2005). However, forecasing echniques are generally no pu down in formal documens, and counry pracices are ofen a mix of idiosyncraic budge pracices and influences from legacy sysems. Too much reliance on few mehods in forecasing revenue of he governmen of Nepal is considered o be less efficien in capuring rue DGP of revenue sequence. No a remarkable exercise has been carried ou in idenifying appropriae mehodology of revenue forecasing from hose insiuions involved in revenue forecasing a presen and here is a lack of privae insiuional forecaser of revenue in he economy. As a resul, here is an over-esimaion or underesimaion of he revenue of he governmen. The forecas error as percenage of GDP shows downward rend wih erraic movemen as represened by forecas error or bias as shown in Diagram. Revenue forecas shows upward biased before FY 200/02 and downward biased hereafer in Nepal. Realizing he facs ha any misspecificaion of appropriae forecasing echniques ha leads o much error in revenue forecasing as moivaing facor of his sudy. In ligh of his fac, he objecives of his paper is o ideniy appropriae mehods for revenue forecasing using monhly oal revenue sequence and rank he mehods under scruiny based on some saisical crieria. Following five imporan mehods of forecasing under consideraion, his sudy found wo mehods namely SARIMA and Winer as he represenaive mehods of revenue forecasing in Nepal. The res of he paper is organized as follows. Nex secion presens

3 Governmen Revenue Forecasing in Nepal 49 explanaion of each of he five mehods under he heading mehodology. Secion III provides resuls and analysis. Finally, he las secion draws he conclusion. II. METHODOLOGY In caegorizing forecasing mehodologies, wo broad approaches can be disinguished. Time series forecasing aemps in predicing he values of a variable from he pas values of he same variable. In conras o he ime series approach economeric forecasing is based on a regression model ha relaes one or more dependen variables o a number of independen variables. The ime series approach has generally been found o be superior o he economeric approach when shor-run predicions are made (Ramanahan, 2002). In his paper, use is made of ime series forecasing approach uilizing level daa of monhly oal revenue series saring from 997 o 202. Las 24 ou of oal 92 observaions are aken o check he accuracy of he forecasing mehods employed in his paper. An ex-ane forecass of 24 observaions are presened in he Appendix. Boh he cumulaive as well as ne monhly forecass are presened uilizing each of he mehods of forecasing employed. The iniial period of sample in FY 997/98 has been chosen based on he year when he governmen of Nepal adoped Value Added Tax as a landmark reform in revenue srucure in Nepal. Followings are he explanaion of basic characerisics of each of he seleced se of mehods ha are used for forecasing in his paper. Hol Mehod : The forecasing mehod developed by Hol (957) is one popular smoohing echnique of forecasing. The wo-parameer exponenial smoohing echnique developed by Hol is a modified mehod of simple exponenial smoohing formula of ~ y ( ) ~ = α y + α y ; where > α > 0 incorporaing average changes in he long-run rend (increase or decline) of he sequence y }. Here, ~ y } is smoohed sequence. Hol { mehods is superior o exponenial smoohing echnique ha former mehod incorporaes rend in he smoohing series. The smoohed or esimaed series is derived by using wo recursive equaions as given in equaion () and (2). The smoohness of he series depends on wo smoohing parameers, α and β boh of which mus lie beween 0 and, ha is, he smaller are α and β he heavier is he smoohing (Makridikis, Wheelwrigh and Hyndman, 998). ~ y = + ( )( ~ + α y α y r ) ;where, > α > 0 () r ~ ~ = β ( y y ) + ( β ) r ; where, > β > 0 (2) y ˆ = ~ y + lr (3) + l T T Here, y~ denoes an esimae of he level of he series a ime and r denoes an esimae of he slope of he series a ime. Equaion (2) adjuss y~ direcly for he rend of he previous period, r by adding i o he las smoohed value ~ y. Equaion (3) is used o forecas l. {

5 Governmen Revenue Forecasing in Nepal 5 (DeLurgio, 998). The sandard noaion idenifies he order of AR by p, I by d and MA by q. An exension of seasonal influence in ARIMA model is represened by SARIMA specificaion. A mixure of AR, I and MA formulaion is known as ARMA (p,d,q) where difference (d) is done before ARMA is specified. The general form of ARIMA model is: y = β y + β2 y β p y p + ε φε φε 2... φqε q () Seasonaliy in a ime series is a regular paern of changes ha repeas over S ime periods, where S defines he number of ime periods unil he paern repeas again. The ARIMA noaion can be exended readily o handle seasonal aspecs, and he general shorhand noaion is ARIMA (p,d,q) (P,D.Q)s (Pindyck and Rubinfeld (997). In a seasonal ARIMA model, seasonal AR and MA erms predic y using daa values and errors a imes wih lags ha are muliples of S (he span of he seasonaliy). Wih monhly daa (S=2), and seasonal firs order auoregressive model would use y 2 o predic y. Variance nonsainary in he ime series is handled by logarihmic ransformaion before SARIMA mehod is adoped. Growh Rae Mehod: Revenue forecasing based on year-on-year growh rae is supposed o capure seasonal influence. The forecas of period +s is calculaed based on average of pas five years (year-on-year) growh raes from period. The increase/decrease of he forecased revenue deermines increase/decrease of forecas revenue from period, ha is, condiional forecass. The formulas for growh mehod are presened in equaion () and (2). r = n n y y ( y i= yˆ = y + ( y * n )*00 (2) + r) /00 (3) Measures of accuracy for forecasing which are free of scale of he daa are adoped in his paper. Two popular relaive measures as frequenly used in measuring accuracy of forecas are Mean Percenage Error (MPE) and Mean Absolue Percenage Error (MAPE) where Percenage Error (PE) is calculaed using he formula Y F PE = *00 (4) Y n MPE = PE (5) n = n MAPE = PE (6) n = The mehods explained above are considered appropriae o capure daa generaing process of oal hisorical revenue series in his paper. While selecing appropriae mehods, due emphasis will be given o hose mehods ha incorporae rend and seasonal

6 52 NRB ECONOMIC REVIEW componen in a ime series analysis. The cyclical componen has no been decomposed from rend componen in his analysis. III. RESULTS AND ANALYSIS In he presen paper, I have conduced shor-erm revenue forecas of he governmen of Nepal for 24 monhs saring from Augus 202 o Augus 204 (i.e. wo years) based on 92 hisorical monhly revenue series beginning from Augus 997 o July 202. A visual inspecion of revenue series in Figure 2 reveals some sylized facs ha he daa generaing process of monhly revenue series clearly shows upward rend accompanied wih monhly seasonal paern. Revenue series shows also ime varying variance over he period. The seasonal paern is no clearly visible in he Diagram 2 as he diagram covers whole sample period. In order o be more specific, he seasonal paern is displayed by he monhly daa for he FY 20/2 reveals clear seasonal paern in he monh of January (six monh), April (nine monh) and July (welve monh). Such seasonal paern can be applicable for he inference of monhly seasonal paern for oher FYs as depiced in Figure 3. The reason for such seasonal influence of revenue mobilizaion in hose monhs is ha he corporae eniies in Nepal are direced o pay declared ax ino hree-insallmen each year including 40% ill mid-january (six monh), 70% ill mid-april (nine monh) and 00% ill mid-july (Twelve monh). I have uilized five equally compeiive mehods ha are applicable for forecasing in case of a ime series daa characerizing period-o-period upward rend, seasonal paern and ime-varying variance. Those mehods include (a) Hol mehod, (b) Winer mehod, (c) decomposiion mehod (d) SARIMA mehod, and (e) growh rae mehod. Among hose mehods, Hol and Winer are he smoohing mehods of forecasing ime series. The esimaed values of he smoohing parameers of α, β and γ deermine he forecas values in hese mehods. As α, β and γ represen smoohing, rend and seasonal parameers respecively, he esimaed values of hose parameers uilizing whole sample daa of presen analysis are presened in Table. The crierion for he selecion of hose parameers is he minimum mean sum of squared error.

8 54 NRB ECONOMIC REVIEW oher coefficiens of monhly dummies including consan erm and rend componen are found saisically significanly differen from zero. Subsiuing he values of esimaed parameers in he corresponding regression equaon looks like: ln(rev ) = T + 0.0D S2 + 0.D D D D7 = (45.9) (47.38) (0.7) (.54) (4.8) (2.80) (9.6) (2.) 0.3D D D D+ 0.89D2 (24) (.98) (6.26) (3.63) (5.30) (3.8) As shown in Diagram 3 of Appendix II, ex-pos forecass of revenue are racking well o he acual revenues from his mehod oo. The revenue forecasing under his mehod are presened in Table of Appendix I. The ARIMA mehod exended wih seasonal componens represened by SARIMA has been uilized in his paper by assuming ha he revenue series under review characerizes seasonal influence. While using SARIMA mehod, revenue series has been convered ino logarihm o he base e before is use in he analysis o capure variance nonsaionary. The SARIMA (0,0,0)(,0,0) 2 is he final specificaion based on idenificaion and diagnosic checking of he mehod. As such he logarihmic 2 h order difference wihou consan erm is he robus represenaion of he model as: ln(rev ) =.02*ln(Rev) 2 (25) = (688.55) Above specificaion yields very good racking of revenue forecass o acual revenue as shown in Diagram 4 of Appendix II. Applying he growh rae mehod, revenue forecasing is deermined by he increase/decrease of five years average of year-on-year growh raes of monhly revenue. The ex-ane forecas for consecuive monhs in he fuure dae are considered condiional forecas based on he forecased revenue a he same monh las year, ha is, i is he ieraive process. Based on his mehod, he forecased revenue during he in-sample period is found saisfacory as depiced by he forecas revenues ha are well racking he acual revenues as shown in he Diagram 5 of Appendix II. The basis for he selecion of appropriae mehods of revenue forecasing in his paper, as quaniaive measure, is he minimum values of MPE and MAPE saisics for each mehod. For his purpose, MPE and MAPE have been calculaed based on laes 24 observaions of forecas errors derived from he difference of acual and esimaed values. The mehod ha obains values of MPE and MAPE close o zero is considered he bes mehod. The MPE and MAPE for each mehod are presened in Table 2.

9 Governmen Revenue Forecasing in Nepal 55 Table 2: Saisical Measures of Model Accuracy (996 Augus o 202 July) S. No. Saisical mehods Mean Percenage Error (MPE) Mean Absolue Percenage Error (MAPE). Hol Mehod Winer Mehod Regression Decomposiion Mehod 4. SARIMA Mehod Growh Rae Mehod Ou of five compeing mehods, wo mehods including Hol mehod, Decomposiion mehod are found less saisfacory mehods in erms of minimum MPE and MAPE crieria. On he remaining hree mehods, growh rae mehod is ranked hird. SARIMA mehod and Winer mehods rank firs and second posiion respecively. The MPE and MAPE for SARIMA mehod are and 6.22 respecively whereas for Winer mehod hey are -.27 and 6.24 respecively. As boh he Winer and SARIMA mehods have buil-in characer o capure he seasonal influence in forecasing, hese mehods can be he represenaive mehods of forecasing governmen revenue in Nepal. Boh he monhly ne and monhly cumulaive forecass for he FY 202/3 and FY 203/4 are presened in Table and 2 of Appendix I. The cumulaive forecas revenues for he FY 202/3 and FY203/4 incorporaing all he five mehods are dragged in Table 3 from Table 2 of Appendix I o inerpree some ineresing conclusions. As SARIMA mehod is ranked firs among he five alernaive mehods under rial, he cumulaive revenue forecass accouns o Rs billion and Rs billion respecively in he FY202/3 and FY203/4. I yields year-on-year growh raes of 4.8 percen and 5.7 percen respecively in FY 202/3 and FY203/4. Table 3: Cumulaive Revenue Foecass and Growh Raes FY Cumulaive Forecas (Rs in Million Percenage Change Decomposiion Growh Decomp- Growh Hol Winer ARIMA Rae Hol Winer osiion ARIMA Rae 20/ / / Similarly, he cumulaive forecas revenues using Winer mehod, as i is found second bes mehod in his paper, are Rs billion and billion for he FY202/3 and FY203/4 respecively. The growh rae is projeced o be increased by 4.8 percen in FY 202/3 and 5.7 percen in FY203/4 according o his mehod. Las bu no he leas, wha i can be concluded in his paper is ha Growh rae mehod is found overly opimisic whereas Decomposiion mehod underesimaes he forecass. Hol mehod is ruled-ou because i does no capure seasonal influence. Therefore, among he five compeiive mehods under scruiny in his paper, Winer and ARIMA are found suiable for revenue projecion based on saisical crieria specified in his paper.

10 56 NRB ECONOMIC REVIEW However, he conclusion drawn in his paper depends on he use of five mehods of forecasing only. Complex forecasing mehods which capure cyclical influence in revenue mobiliaion are ou of perview in his paper. Since he moivaion of he sudy is o use ime series analysis in revenue forecsing as agains he condiional foecass mehod, laer mehod may obain differen resuls. IV. CONCLUSION Governmen revenue forecasing is an imporan aspec in he design and execuion of sound fiscal policies. The forecas error as percen of GDP over he sudy period is downward rending. As a consequence, here is an over-esimaion of revenue followed by under-esimaion. Furher, here is an erraic movemen of forecas error oo. As he exising mehods of revenue forecasing in Nepal is limied o growh rae basis and hence miss he arge, he objecive of he paper is o idenify appropriae mehodology of revenue forecasing. This paper uilizes monhly revenue series including 92 observaions saring from 997 o 202 or he analysis. Ou of he five popular echniques scruinized in his paper, wo compeing mehods including Winer and SARIMA mehods are found o be appropriae for he revenue forecasing in Nepal. However, SARIMA mehod is found albei superior han Winer mehod in erm of minimum MPE and MAPE crierion. Using SARIMA mehod, oal revenue is forecased o be increase by 5.7 in FY 202/3 and 4.8 percen in FY 203/4. The resuls of revenue forecasing in his paper may vary depending on he use of mehods ha capure cyclical componen of revenue series. Furher, he mehods of condiional forecasing are no applied here and hence may give differen resuls. Therefore, in ligh of hese limiaions, he forecasing aemps in his paper have opened an avenue for he sysemaic analysis of revenue forecasing using several mehods raher han depending on exising growh rae mehod. REFERENCES Auerbach, A J "On he Performance and Use of Governmen Revenue Forecass." Universiy of California, Berkeley and NBER, USA. Box, G.P.E. and G. M. Jenkins Time Series Analysis, Forecasing and Conrol. 3 rd ed. Englewood Cliffs, NJ: Prenice-Hall. Danninger, S "Revenue Forecass as Performance Targes." IMF Working Paper No. WP/05/4, IMF, Washingon, D.C. DeLurgio, S.A Forecasing Principles and Applicaions. Irwin McGraw-Hill Book Co., Singapore. Gujarai, D.N Basic Economerics 4 h ediion, Taa McGraw-Hill Publishing Company Ld., New Delhi. Hol, C.C "Forecasing Seasonal and Trends by Exponenially Weighed Moving Averages, Office of Naval Research, Research Memorandum No.52.

12 58 NRB ECONOMIC REVIEW Appendix I: Tables Table : Monhly Revenue Forecass for 202/3 and 203/4 Mid-Monhs Hol Mehod Winer Mehod Decomposiion Mehod ARIMA Mehod (Rs in million) Growh Rae Mehod Ne Forecased Revenue for he FY 202/3 Augus Sepember Ocober November December January February March April May June July Ne Forecased Revenue for he FY 203/4 Augus Sepember Ocober November December January February March April May June July

13 Governmen Revenue Forecasing in Nepal 59 Table 2: Monhly Revenue Forecas (Cumulaive) for 202/3 and 203/4 Rs in million Mid-Monhs Hol Mehod Winer Mehod Decomposiion Mehod ARIMA Mehod Growh Rae Mehod Monhly Revenue Forecas (Cumulaive) for 202/3 Augus Sepember Ocober November December January February March April May June July Monhly Revenue Forecas (Cumulaive) for 203/4 Augus Sepember Ocober November December January February March April May June July

MACROECONOMIC FORECASTS AT THE MOF A LOOK INTO THE REAR VIEW MIRROR The firs experimenal publicaion, which summarised pas and expeced fuure developmen of basic economic indicaors, was published by he Minisry

Chaper Suden Lecure Noes - Chaper Goals QM: Business Saisics Chaper Analyzing and Forecasing -Series Daa Afer compleing his chaper, you should be able o: Idenify he componens presen in a ime series Develop

An empirical analysis abou forecasing Tmall air-condiioning sales using ime series model Yan Xia Deparmen of Mahemaics, Ocean Universiy of China, China Absrac Time series model is a hospo in he research

House Price Index (HPI) The price index of second hand houses in Colombia (HPI), regisers annually and quarerly he evoluion of prices of his ype of dwelling. The calculaion is based on he repeaed sales

ABSTRACT Time Series Analysis Using SAS R Par I The Augmened Dickey-Fuller (ADF) Tes By Ismail E. Mohamed The purpose of his series of aricles is o discuss SAS programming echniques specifically designed

Forecasing Including an Inroducion o Forecasing using he SAP R/3 Sysem by James D. Blocher Vincen A. Maber Ashok K. Soni Munirpallam A. Venkaaramanan Indiana Universiy Kelley School of Business February

Premium Income of Indian Life Insurance Indusry A Toal Facor Produciviy Approach Ram Praap Sinha* Subsequen o he passage of he Insurance Regulaory and Developmen Auhoriy (IRDA) Ac, 1999, he life insurance

These noes largely concern auocorrelaion Issues Using OLS wih Time Series Daa Recall main poins from Chaper 10: Time series daa NOT randomly sampled in same way as cross secional each obs no i.i.d Why?

Quarerly Repor on he Euro Area 3/202 II.. Deb reducion and fiscal mulipliers The deerioraion of public finances in he firs years of he crisis has led mos Member Saes o adop sizeable consolidaion packages.

Mahemaics in Pharmacokineics Wha and Why (A second aemp o make i clearer) We have used equaions for concenraion () as a funcion of ime (). We will coninue o use hese equaions since he plasma concenraions

1 Ocober 23 Measuring he Services of Propery-Casualy Insurance in he IPAs Changes in Conceps and Mehods By Baoline Chen and Dennis J. Fixler A S par of he comprehensive revision of he naional income and

YEN FUTURES: EXAMINING HEDGING EFFECTIVENESS BIAS AND CROSS-CURRENCY HEDGING RESULTS ROBERT T. DAIGLER FLORIDA INTERNATIONAL UNIVERSITY SUBMITTED FOR THE FIRST ANNUAL PACIFIC-BASIN FINANCE CONFERENCE The

Chaper 6: Business Valuaion (Income Approach) Cash flow deerminaion is one of he mos criical elemens o a business valuaion. Everyhing may be secondary. If cash flow is high, hen he value is high; if he

Inroducion Chaper 14: Dynamic D-S dynamic model of aggregae and aggregae supply gives us more insigh ino how he economy works in he shor run. I is a simplified version of a DSGE model, used in cuing-edge

Relaionships beween Sock Prices and Accouning Informaion: A Review of he Residual Income and Ohlson Models Sco Pirie* and Malcolm Smih** * Inernaional Graduae School of Managemen, Universiy of Souh Ausralia

Individual Healh Insurance April 30, 2008 Pages 167-170 We have received feedback ha his secion of he e is confusing because some of he defined noaion is inconsisen wih comparable life insurance reserve

CHAPTER 31 Reporing o Managemen Inroducion The success or oherwise of any business underaking depends primarily on earning revenue ha would generae sufficien resources for sound growh. To achieve his objecive,

Why Do Real and Nominal Invenory-Sales Raios Have Differen Trends? By Valerie A. Ramey Professor of Economics Deparmen of Economics Universiy of California, San Diego and Research Associae Naional Bureau

A Brief Inroducion o he Consumpion Based Asse Pricing Model (CCAPM We have seen ha CAPM idenifies he risk of any securiy as he covariance beween he securiy's rae of reurn and he rae of reurn on he marke